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Article: Solving long time-horizon dynamic optimal power flow of large-scale power grids with direct solution method

TitleSolving long time-horizon dynamic optimal power flow of large-scale power grids with direct solution method
Authors
Issue Date2014
PublisherIET.
Citation
Generation, Transmission & Distribution, IET, 2014, v. 8 n. 5, p. 895-906 How to Cite?
AbstractDynamic optimal power flow (DOPF) is an extension of optimal power flow for the optimal generation dispatch in a given time-horizon. The dynamic constraints bring tremendous numerical difficulties in solving this model. With particular attention to handle dynamic constraints, an efficient method has been presented for directly solving the large-scale DOPF Karush-Kuhn-Tucker (KKT) system arising from the primal-dual interior point method. First, the reduced KKT system is derived, showing that dynamic constraints lead to non-zeros in off-diagonal parts in the coefficient of KKT system. Then, the efficiency of the algorithm is improved by two measures: (i) to utilise the Cholesky factorisation algorithm, a constant diagonal perturbation is introduced in the positive-indefinite KKT coefficient and (ii) efficient reordering algorithms are identified and integrated in the sparse direct solver to improve the efficiency. Case studies on the IEEE 118-bus system over 24-96 time intervals are presented. These case studies show that the proposed method has a significant speed-up than decomposed interior point methods. The proposed method has also been successfully applied in Chinese realistic large-scale power grids. Two realistic case studies are reported. Both realistic cases have over 100 000 decision variables.
Persistent Identifierhttp://hdl.handle.net/10722/197840
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorQIN, Zen_US
dc.contributor.authorHou, Yen_US
dc.contributor.authorLu, Een_US
dc.contributor.authorLuo, Cen_US
dc.contributor.authorCheng, Sen_US
dc.date.accessioned2014-06-02T15:15:13Z-
dc.date.available2014-06-02T15:15:13Z-
dc.date.issued2014en_US
dc.identifier.citationGeneration, Transmission & Distribution, IET, 2014, v. 8 n. 5, p. 895-906en_US
dc.identifier.urihttp://hdl.handle.net/10722/197840-
dc.description.abstractDynamic optimal power flow (DOPF) is an extension of optimal power flow for the optimal generation dispatch in a given time-horizon. The dynamic constraints bring tremendous numerical difficulties in solving this model. With particular attention to handle dynamic constraints, an efficient method has been presented for directly solving the large-scale DOPF Karush-Kuhn-Tucker (KKT) system arising from the primal-dual interior point method. First, the reduced KKT system is derived, showing that dynamic constraints lead to non-zeros in off-diagonal parts in the coefficient of KKT system. Then, the efficiency of the algorithm is improved by two measures: (i) to utilise the Cholesky factorisation algorithm, a constant diagonal perturbation is introduced in the positive-indefinite KKT coefficient and (ii) efficient reordering algorithms are identified and integrated in the sparse direct solver to improve the efficiency. Case studies on the IEEE 118-bus system over 24-96 time intervals are presented. These case studies show that the proposed method has a significant speed-up than decomposed interior point methods. The proposed method has also been successfully applied in Chinese realistic large-scale power grids. Two realistic case studies are reported. Both realistic cases have over 100 000 decision variables.en_US
dc.languageengen_US
dc.publisherIET.en_US
dc.relation.ispartofGeneration, Transmission & Distribution, IETen_US
dc.titleSolving long time-horizon dynamic optimal power flow of large-scale power grids with direct solution methoden_US
dc.typeArticleen_US
dc.identifier.emailHou, Y: yhhou@eee.hku.hken_US
dc.identifier.authorityHou, Y=rp00069en_US
dc.identifier.doi10.1049/iet-gtd.2013.0659en_US
dc.identifier.hkuros229094en_US
dc.identifier.volume8en_US
dc.identifier.issue5en_US
dc.identifier.spage895en_US
dc.identifier.epage906en_US
dc.identifier.isiWOS:000335837000011-

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